mirror of
https://github.com/dograh-hq/dograh.git
synced 2026-06-22 08:38:13 +02:00
feat: add Tuner Integration to Dograh (#311)
* Add tuner integration * bump pipecat version * chore: update pipecat submodule to match upstream and use tuner-pipecat-sdk 0.2.0 Update pipecat submodule from 0.0.109.dev23 to 13e98d0d9 (the exact commit upstream dograh-hq/dograh uses after v1.30.1). This installs pipecat-ai as 1.1.0.post277 via setuptools_scm, satisfying tuner-pipecat-sdk 0.2.0's pipecat-ai>=1.0.0 requirement. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * wire tuner * feat: refactor integrations into self contained packages * chore: simplify ensure_public_access_token * fix: remove NodeSpec and make DTOs the source of truth * feat: send relevant signal to mcp using to_mcp_dict * fix: fix tests * cleanup: remove nango integrations * feat: add agents.md for integrations --------- Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> Co-authored-by: Abhishek Kumar <abhishek@a6k.me>
This commit is contained in:
Mohamed-Mamdouh
GitHub
parent
afa78fe859
commit
5f28c1b2a9
93 changed files with 3388 additions and 3414 deletions
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@ -7,7 +7,7 @@ script in `api/services/admin_utils/local_exec.py` is the production
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consumer.
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"""
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from api.services.workflow.node_specs import REGISTRY
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from api.services.workflow.node_specs import all_specs
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def _build_type_rules() -> tuple[set[str], set[str]]:
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@ -16,14 +16,14 @@ def _build_type_rules() -> tuple[set[str], set[str]]:
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(max_incoming == 0)."""
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src_forbidden: set[str] = set()
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tgt_forbidden: set[str] = set()
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for name, spec in REGISTRY.items():
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for spec in all_specs():
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gc = spec.graph_constraints
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if gc is None:
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continue
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if gc.max_outgoing == 0:
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src_forbidden.add(name)
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src_forbidden.add(spec.name)
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if gc.max_incoming == 0:
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tgt_forbidden.add(name)
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tgt_forbidden.add(spec.name)
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return src_forbidden, tgt_forbidden
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File diff suppressed because it is too large
Load diff
19
api/services/workflow/node_data.py
Normal file
19
api/services/workflow/node_data.py
Normal file
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@ -0,0 +1,19 @@
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from __future__ import annotations
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from pydantic import BaseModel
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from api.services.workflow.node_specs._base import PropertyType
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from api.services.workflow.node_specs.model_spec import spec_field
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class BaseNodeData(BaseModel):
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name: str = spec_field(
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...,
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min_length=1,
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ui_type=PropertyType.string,
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display_name="Name",
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description="Short identifier shown in the canvas and call logs.",
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required=True,
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)
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is_start: bool = spec_field(default=False, spec_exclude=True)
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is_end: bool = spec_field(default=False, spec_exclude=True)
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@ -1,10 +1,8 @@
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"""Node specification registry.
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Adding a new node type:
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1. Create a new module under this package, define a `SPEC: NodeSpec`.
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2. Add it to the imports + REGISTRY below.
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3. The Pydantic discriminated-union variant in dto.py must use the same
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`name` value as `SPEC.name`.
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Core node specs are generated from the workflow DTO models. Third-party
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integration node specs live under `api.services.integrations/<name>/` and
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register through the integration registry so they don't need edits here.
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"""
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from __future__ import annotations
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@ -21,8 +19,10 @@ from api.services.workflow.node_specs._base import (
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PropertyType,
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evaluate_display_options,
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)
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from api.services.workflow.node_specs.model_spec import build_spec
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REGISTRY: dict[str, NodeSpec] = {}
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_CORE_SPECS_LOADED = False
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def register(spec: NodeSpec) -> NodeSpec:
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@ -38,12 +38,23 @@ def register(spec: NodeSpec) -> NodeSpec:
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def get_spec(name: str) -> NodeSpec | None:
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return REGISTRY.get(name)
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_ensure_core_registered()
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if name in REGISTRY:
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return REGISTRY[name]
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from api.services.integrations import get_node_spec
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return get_node_spec(name)
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def all_specs() -> list[NodeSpec]:
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"""All registered specs, sorted by name for stable output."""
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return [REGISTRY[name] for name in sorted(REGISTRY)]
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_ensure_core_registered()
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from api.services.integrations import all_node_specs
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specs = {spec.name: spec for spec in REGISTRY.values()}
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specs.update({spec.name: spec for spec in all_node_specs()})
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return [specs[name] for name in sorted(specs)]
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__all__ = [
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@ -64,19 +75,15 @@ __all__ = [
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]
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# Side-effect imports — each module's `register(SPEC)` call populates REGISTRY.
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# Keep at module bottom so the registry helpers are defined first.
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from api.services.workflow.node_specs import ( # noqa: E402, F401
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agent,
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end_call,
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global_node,
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qa,
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start_call,
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trigger,
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webhook,
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)
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def _ensure_core_registered() -> None:
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global _CORE_SPECS_LOADED
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if _CORE_SPECS_LOADED:
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return
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# Wire up registrations from the SPEC constants in each module.
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for _module in (start_call, agent, end_call, global_node, trigger, webhook, qa):
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register(_module.SPEC)
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del _module
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from api.services.workflow.dto import _CORE_NODE_DATA_CLASSES
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for model_cls in _CORE_NODE_DATA_CLASSES.values():
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if model_cls.__node_spec_metadata__.name in REGISTRY:
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continue
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register(build_spec(model_cls))
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_CORE_SPECS_LOADED = True
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@ -1,9 +1,9 @@
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"""Spec schema for node definitions.
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A `NodeSpec` is the single source of truth for a node type. It drives:
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- Pydantic validation (the per-type DTOs in dto.py mirror these property types)
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- The generic UI renderer (frontend reads specs via /api/v1/node-types)
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- The LLM SDK (constructors and JSON-Schema derived from these specs)
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`NodeSpec` is the serialized contract exposed to the frontend, MCP tools, and
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SDKs. Core workflow node specs are generated from the DTO models plus
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model-attached metadata; integration packages may generate them the same way or
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register a prebuilt spec object.
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Every property's `description` is LLM-readable copy — treat it as production
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documentation, not internal notes. Spec lint enforces non-empty descriptions
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@ -122,6 +122,16 @@ class PropertyOption(BaseModel):
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model_config = ConfigDict(extra="forbid")
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def to_mcp_dict(self) -> dict[str, Any]:
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"""Lean projection for `get_node_type`: the `value` an LLM writes in
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code, plus a `description` when one carries real meaning. The UI
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`label` is dropped — it's the option's display string, never used
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when authoring."""
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out: dict[str, Any] = {"value": self.value}
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if self.description:
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out["description"] = self.description
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return out
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class PropertySpec(BaseModel):
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"""Single field on a node.
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@ -175,6 +185,43 @@ class PropertySpec(BaseModel):
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model_config = ConfigDict(extra="forbid")
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def to_mcp_dict(self) -> dict[str, Any]:
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"""Lean projection of this property for the `get_node_type` MCP tool.
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Keeps only what an LLM needs to author a valid value: name, type,
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description, llm_hint, requiredness, default, enum options, nested
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row properties, and validation bounds. UI-rendering concerns
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(`display_name`, `placeholder`, `display_options`, `editor`,
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`extra`) and null/empty fields are omitted — they're noise in the
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model's context and never appear in authored SDK code.
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"""
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out: dict[str, Any] = {
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"name": self.name,
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"type": self.type.value,
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"description": self.description,
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}
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if self.llm_hint:
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out["llm_hint"] = self.llm_hint
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if self.required:
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out["required"] = True
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if self.default is not None:
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out["default"] = self.default
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if self.options:
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out["options"] = [opt.to_mcp_dict() for opt in self.options]
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if self.properties:
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out["properties"] = [prop.to_mcp_dict() for prop in self.properties]
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for constraint in (
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"min_value",
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"max_value",
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"min_length",
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"max_length",
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"pattern",
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):
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value = getattr(self, constraint)
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if value is not None:
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out[constraint] = value
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return out
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PropertySpec.model_rebuild()
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@ -222,3 +269,33 @@ class NodeSpec(BaseModel):
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graph_constraints: Optional[GraphConstraints] = None
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model_config = ConfigDict(extra="forbid")
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def to_mcp_dict(self) -> dict[str, Any]:
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"""Lean projection of this spec for the `get_node_type` MCP tool.
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Drops node-level UI metadata (`display_name`, `category`, `icon`,
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`version`) and the per-property rendering concerns trimmed by
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`PropertySpec.to_mcp_dict`, leaving just the authoring-relevant
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schema the LLM consumes when composing a workflow. The full spec is
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still served verbatim to the frontend renderer (REST `node-types`
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route) and the SDK codegen / TS validator (`ts_bridge`), which need
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the dropped fields.
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"""
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out: dict[str, Any] = {
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"name": self.name,
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"description": self.description,
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}
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if self.llm_hint:
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out["llm_hint"] = self.llm_hint
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out["properties"] = [prop.to_mcp_dict() for prop in self.properties]
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if self.examples:
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out["examples"] = [
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ex.model_dump(mode="json", exclude_none=True) for ex in self.examples
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]
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if self.graph_constraints:
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constraints = self.graph_constraints.model_dump(
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mode="json", exclude_none=True
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)
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if constraints:
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out["graph_constraints"] = constraints
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return out
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@ -1,168 +0,0 @@
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"""Spec for the Agent node — the workhorse mid-call node where the LLM
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executes a focused conversational step with optional tools and documents."""
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from api.services.workflow.node_specs._base import (
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DisplayOptions,
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GraphConstraints,
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NodeCategory,
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NodeExample,
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NodeSpec,
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PropertyOption,
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PropertySpec,
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PropertyType,
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)
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SPEC = NodeSpec(
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name="agentNode",
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display_name="Agent Node",
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description="Conversational step — the LLM runs one focused exchange.",
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llm_hint=(
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"Mid-call step executed by the LLM. Most workflows are a chain of "
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"agent nodes connected by edges that describe transition conditions. "
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"Each agent node can invoke tools and reference documents."
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),
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category=NodeCategory.call_node,
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icon="Headset",
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properties=[
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PropertySpec(
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name="name",
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type=PropertyType.string,
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display_name="Name",
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description=(
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"Short identifier for this step (e.g., 'Qualify Budget'). "
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"Appears in call logs and edge transition tools."
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),
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required=True,
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min_length=1,
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default="Agent",
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),
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PropertySpec(
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name="prompt",
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type=PropertyType.mention_textarea,
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display_name="Prompt",
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description=(
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"Agent system prompt for this step. Supports "
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"{{template_variables}} from extraction or pre-call fetch."
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),
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required=True,
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min_length=1,
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placeholder="Ask the caller about their budget and timeline.",
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),
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PropertySpec(
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name="allow_interrupt",
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type=PropertyType.boolean,
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display_name="Allow Interruption",
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description=(
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"When true, the user can interrupt the agent mid-utterance. "
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"Set false for non-interruptible disclosures."
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),
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default=True,
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),
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PropertySpec(
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name="add_global_prompt",
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type=PropertyType.boolean,
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display_name="Add Global Prompt",
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description=(
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"When true and a Global node exists, prepends the global "
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"prompt to this node's prompt at runtime."
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),
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default=True,
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),
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PropertySpec(
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name="extraction_enabled",
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type=PropertyType.boolean,
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display_name="Enable Variable Extraction",
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description=(
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"When true, runs an LLM extraction pass on transition out of "
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"this node to capture variables from the conversation."
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),
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default=False,
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),
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PropertySpec(
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name="extraction_prompt",
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type=PropertyType.string,
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display_name="Extraction Prompt",
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description="Overall instructions guiding variable extraction.",
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display_options=DisplayOptions(show={"extraction_enabled": [True]}),
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editor="textarea",
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),
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PropertySpec(
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name="extraction_variables",
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type=PropertyType.fixed_collection,
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display_name="Variables to Extract",
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description=(
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"Each entry declares one variable to capture from the "
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"conversation, with its name, type, and per-variable hint."
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),
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display_options=DisplayOptions(show={"extraction_enabled": [True]}),
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properties=[
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PropertySpec(
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name="name",
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type=PropertyType.string,
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display_name="Variable Name",
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description="snake_case identifier used downstream.",
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required=True,
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),
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PropertySpec(
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name="type",
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type=PropertyType.options,
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display_name="Type",
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description="Data type of the extracted value.",
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required=True,
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default="string",
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options=[
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PropertyOption(value="string", label="String"),
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PropertyOption(value="number", label="Number"),
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PropertyOption(value="boolean", label="Boolean"),
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],
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),
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PropertySpec(
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name="prompt",
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type=PropertyType.string,
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display_name="Extraction Hint",
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description="Per-variable hint describing what to look for.",
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editor="textarea",
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),
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],
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),
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PropertySpec(
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name="tool_uuids",
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type=PropertyType.tool_refs,
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display_name="Tools",
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description="Tools the agent can invoke during this step.",
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llm_hint="List of tool UUIDs from `list_tools`.",
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),
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PropertySpec(
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name="document_uuids",
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type=PropertyType.document_refs,
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display_name="Knowledge Base Documents",
|
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description="Documents the agent can reference during this step.",
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llm_hint="List of document UUIDs from `list_documents`.",
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),
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],
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examples=[
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NodeExample(
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name="qualify_lead",
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data={
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"name": "Qualify Budget",
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"prompt": "Ask about budget and timeline. Capture both before transitioning.",
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"allow_interrupt": True,
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"extraction_enabled": True,
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"extraction_prompt": "Extract budget amount and rough timeline.",
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"extraction_variables": [
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{
|
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"name": "budget_usd",
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"type": "number",
|
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"prompt": "Stated budget in USD",
|
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},
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{
|
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"name": "timeline",
|
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"type": "string",
|
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"prompt": "When they want to start",
|
||||
},
|
||||
],
|
||||
},
|
||||
),
|
||||
],
|
||||
graph_constraints=GraphConstraints(min_incoming=1),
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||||
)
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44
api/services/workflow/node_specs/constants.py
Normal file
44
api/services/workflow/node_specs/constants.py
Normal file
|
|
@ -0,0 +1,44 @@
|
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DEFAULT_QA_SYSTEM_PROMPT = """You are a QA analyst evaluating a specific segment of a voice AI conversation.
|
||||
|
||||
## Node Purpose
|
||||
{{node_summary}}
|
||||
|
||||
## Previous Conversation Context (For start of conversation, previous conversation summary can be empty.)
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{{previous_conversation_summary}}
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|
||||
## Tags to evaluate
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|
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Examine the conversation carefully and identify which of the following tags apply:
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- UNCLEAR_CONVERSATION - The conversation is not coherent or clear, messages don't connect logically
|
||||
- ASSISTANT_IN_LOOP - The assistant asks the same question multiple times or gets stuck repeating itself
|
||||
- ASSISTANT_REPLY_IMPROPER - The assistant did not reply properly to the user's question/query or seems confused by what the user said
|
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- USER_FRUSTRATED - The user seems angry, frustrated, or is complaining about something in the call
|
||||
- USER_NOT_UNDERSTANDING - The user explicitly says they don't understand or repeatedly asks for clarification
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- HEARING_ISSUES - Either party can't hear the other ("hello?", "are you there?", "can you hear me?")
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- DEAD_AIR - Unusually long silences in the conversation (use the timestamps to judge)
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- USER_REQUESTING_FEATURE - The user asks for something the assistant can't fulfill
|
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- ASSISTANT_LACKS_EMPATHY - The assistant ignores the user's personal situation or emotional state and continues pitching or pushing the agenda.
|
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- USER_DETECTS_AI - The user suspects or identifies that they are talking to an AI/robot/bot rather than a real human.
|
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|
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## Call metrics (pre-computed)
|
||||
|
||||
Use these alongside the transcript for your analysis:
|
||||
{{metrics}}
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||||
|
||||
## Output format
|
||||
|
||||
Return ONLY a valid JSON object (no markdown):
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{
|
||||
"tags": [
|
||||
{
|
||||
"tag": "TAG_NAME",
|
||||
"reason": "Short reason with evidence from the transcript"
|
||||
}
|
||||
],
|
||||
"overall_sentiment": "positive|neutral|negative",
|
||||
"call_quality_score": <1-10>,
|
||||
"summary": "1-2 sentence summary of this segment"
|
||||
}
|
||||
|
||||
If no tags apply, return an empty tags list. Always provide sentiment, score, and summary."""
|
||||
|
|
@ -1,141 +0,0 @@
|
|||
"""Spec for the End Call node — terminal node that wraps up a conversation
|
||||
and optionally extracts variables before hangup."""
|
||||
|
||||
from api.services.workflow.node_specs._base import (
|
||||
DisplayOptions,
|
||||
GraphConstraints,
|
||||
NodeCategory,
|
||||
NodeExample,
|
||||
NodeSpec,
|
||||
PropertyOption,
|
||||
PropertySpec,
|
||||
PropertyType,
|
||||
)
|
||||
|
||||
SPEC = NodeSpec(
|
||||
name="endCall",
|
||||
display_name="End Call",
|
||||
description="Closes the conversation and hangs up.",
|
||||
llm_hint=(
|
||||
"Terminal node that politely closes the conversation. Variable "
|
||||
"extraction can run before hangup. A workflow can have multiple "
|
||||
"endCall nodes reached via different edge conditions."
|
||||
),
|
||||
category=NodeCategory.call_node,
|
||||
icon="OctagonX",
|
||||
properties=[
|
||||
PropertySpec(
|
||||
name="name",
|
||||
type=PropertyType.string,
|
||||
display_name="Name",
|
||||
description=(
|
||||
"Short identifier shown in call logs. Should describe the "
|
||||
"ending context (e.g., 'Successful close', 'Polite decline')."
|
||||
),
|
||||
required=True,
|
||||
min_length=1,
|
||||
default="End Call",
|
||||
),
|
||||
PropertySpec(
|
||||
name="prompt",
|
||||
type=PropertyType.mention_textarea,
|
||||
display_name="Prompt",
|
||||
description=(
|
||||
"Agent system prompt for the closing exchange. Supports "
|
||||
"{{template_variables}} from extraction or pre-call fetch."
|
||||
),
|
||||
required=True,
|
||||
min_length=1,
|
||||
placeholder="Thank the caller and confirm next steps before ending the call.",
|
||||
),
|
||||
PropertySpec(
|
||||
name="add_global_prompt",
|
||||
type=PropertyType.boolean,
|
||||
display_name="Add Global Prompt",
|
||||
description=(
|
||||
"When true and a Global node exists, prepends the global "
|
||||
"prompt to this node's prompt at runtime."
|
||||
),
|
||||
default=False,
|
||||
),
|
||||
PropertySpec(
|
||||
name="extraction_enabled",
|
||||
type=PropertyType.boolean,
|
||||
display_name="Enable Variable Extraction",
|
||||
description=(
|
||||
"When true, runs an LLM extraction pass before hangup to "
|
||||
"capture variables from the conversation."
|
||||
),
|
||||
default=False,
|
||||
),
|
||||
PropertySpec(
|
||||
name="extraction_prompt",
|
||||
type=PropertyType.string,
|
||||
display_name="Extraction Prompt",
|
||||
description=(
|
||||
"Overall instructions guiding how variables should be "
|
||||
"extracted from the conversation."
|
||||
),
|
||||
display_options=DisplayOptions(show={"extraction_enabled": [True]}),
|
||||
editor="textarea",
|
||||
),
|
||||
PropertySpec(
|
||||
name="extraction_variables",
|
||||
type=PropertyType.fixed_collection,
|
||||
display_name="Variables to Extract",
|
||||
description=(
|
||||
"Each entry declares one variable to capture from the "
|
||||
"conversation, with its name, data type, and a per-variable "
|
||||
"extraction hint."
|
||||
),
|
||||
display_options=DisplayOptions(show={"extraction_enabled": [True]}),
|
||||
properties=[
|
||||
PropertySpec(
|
||||
name="name",
|
||||
type=PropertyType.string,
|
||||
display_name="Variable Name",
|
||||
description="snake_case identifier used downstream.",
|
||||
required=True,
|
||||
),
|
||||
PropertySpec(
|
||||
name="type",
|
||||
type=PropertyType.options,
|
||||
display_name="Type",
|
||||
description="The data type of the extracted value.",
|
||||
required=True,
|
||||
default="string",
|
||||
options=[
|
||||
PropertyOption(value="string", label="String"),
|
||||
PropertyOption(value="number", label="Number"),
|
||||
PropertyOption(value="boolean", label="Boolean"),
|
||||
],
|
||||
),
|
||||
PropertySpec(
|
||||
name="prompt",
|
||||
type=PropertyType.string,
|
||||
display_name="Extraction Hint",
|
||||
description=(
|
||||
"Per-variable hint describing what to look for in "
|
||||
"the conversation."
|
||||
),
|
||||
editor="textarea",
|
||||
),
|
||||
],
|
||||
),
|
||||
],
|
||||
examples=[
|
||||
NodeExample(
|
||||
name="successful_close",
|
||||
data={
|
||||
"name": "Successful Close",
|
||||
"prompt": "Confirm the appointment time, thank the caller, and end the call.",
|
||||
"add_global_prompt": False,
|
||||
},
|
||||
),
|
||||
],
|
||||
graph_constraints=GraphConstraints(
|
||||
min_incoming=1,
|
||||
min_outgoing=0,
|
||||
max_outgoing=0,
|
||||
),
|
||||
)
|
||||
|
|
@ -1,77 +0,0 @@
|
|||
"""Spec for the Global node — system-level instructions appended to every
|
||||
agent node that opts in via `add_global_prompt`."""
|
||||
|
||||
from api.services.workflow.node_specs._base import (
|
||||
GraphConstraints,
|
||||
NodeCategory,
|
||||
NodeExample,
|
||||
NodeSpec,
|
||||
PropertySpec,
|
||||
PropertyType,
|
||||
)
|
||||
|
||||
SPEC = NodeSpec(
|
||||
name="globalNode",
|
||||
display_name="Global Node",
|
||||
description="Persona/tone appended to every agent node's prompt.",
|
||||
llm_hint=(
|
||||
"System-level prompt appended to every prompted node whose "
|
||||
"`add_global_prompt` is true. Use it for persona, tone, and shared "
|
||||
"rules that apply across the entire conversation. At most one "
|
||||
"global node per workflow."
|
||||
),
|
||||
category=NodeCategory.global_node,
|
||||
icon="Globe",
|
||||
properties=[
|
||||
PropertySpec(
|
||||
name="name",
|
||||
type=PropertyType.string,
|
||||
display_name="Name",
|
||||
description=(
|
||||
"Short identifier shown in the canvas and call logs. Has no "
|
||||
"runtime effect."
|
||||
),
|
||||
required=True,
|
||||
min_length=1,
|
||||
default="Global Node",
|
||||
),
|
||||
PropertySpec(
|
||||
name="prompt",
|
||||
type=PropertyType.mention_textarea,
|
||||
display_name="Global Prompt",
|
||||
description=(
|
||||
"Text appended to every prompted node's system prompt when "
|
||||
"that node has `add_global_prompt=true`. Supports "
|
||||
"{{template_variables}}."
|
||||
),
|
||||
required=True,
|
||||
min_length=1,
|
||||
placeholder="You are a friendly assistant calling on behalf of {{company_name}}.",
|
||||
default=(
|
||||
"You are a helpful assistant whose mode of interaction with "
|
||||
"the user is voice. So don't use any special characters which "
|
||||
"can not be pronounced. Use short sentences and simple language."
|
||||
),
|
||||
),
|
||||
],
|
||||
examples=[
|
||||
NodeExample(
|
||||
name="basic_persona",
|
||||
description="Establishes a consistent persona across the call.",
|
||||
data={
|
||||
"name": "Persona",
|
||||
"prompt": (
|
||||
"You are Sarah, a polite and warm representative from "
|
||||
"Acme Corp. Always thank the caller for their time and "
|
||||
"speak in short conversational sentences."
|
||||
),
|
||||
},
|
||||
),
|
||||
],
|
||||
graph_constraints=GraphConstraints(
|
||||
min_incoming=0,
|
||||
max_incoming=0,
|
||||
min_outgoing=0,
|
||||
max_outgoing=0,
|
||||
),
|
||||
)
|
||||
404
api/services/workflow/node_specs/model_spec.py
Normal file
404
api/services/workflow/node_specs/model_spec.py
Normal file
|
|
@ -0,0 +1,404 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass
|
||||
from dataclasses import field as dataclass_field
|
||||
from enum import Enum
|
||||
from types import NoneType
|
||||
from typing import Any, Callable, Literal, get_args, get_origin
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
from pydantic.fields import FieldInfo, PydanticUndefined
|
||||
|
||||
from api.services.workflow.node_specs._base import (
|
||||
DisplayOptions,
|
||||
GraphConstraints,
|
||||
NodeCategory,
|
||||
NodeExample,
|
||||
NodeSpec,
|
||||
PropertyOption,
|
||||
PropertySpec,
|
||||
PropertyType,
|
||||
)
|
||||
|
||||
_SPEC_FIELD_META_KEY = "__dograh_spec_field__"
|
||||
_UNSET = object()
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class NodeSpecMetadata:
|
||||
name: str
|
||||
display_name: str
|
||||
description: str
|
||||
category: NodeCategory
|
||||
icon: str
|
||||
llm_hint: str | None = None
|
||||
version: str = "1.0.0"
|
||||
examples: tuple[NodeExample, ...] = ()
|
||||
graph_constraints: GraphConstraints | None = None
|
||||
property_order: tuple[str, ...] = ()
|
||||
field_overrides: dict[str, dict[str, Any]] = dataclass_field(default_factory=dict)
|
||||
|
||||
|
||||
def spec_field(
|
||||
*field_args: Any,
|
||||
ui_type: PropertyType | str | None = None,
|
||||
display_name: str | None = None,
|
||||
llm_hint: str | None = None,
|
||||
required: bool | None = None,
|
||||
spec_default: Any = _UNSET,
|
||||
placeholder: str | None = None,
|
||||
display_options: DisplayOptions | None = None,
|
||||
options: list[PropertyOption] | None = None,
|
||||
editor: str | None = None,
|
||||
extra: dict[str, Any] | None = None,
|
||||
spec_exclude: bool = False,
|
||||
min_value: float | None = None,
|
||||
max_value: float | None = None,
|
||||
min_length: int | None = None,
|
||||
max_length: int | None = None,
|
||||
pattern: str | None = None,
|
||||
**field_kwargs: Any,
|
||||
):
|
||||
json_schema_extra = dict(field_kwargs.pop("json_schema_extra", {}) or {})
|
||||
json_schema_extra[_SPEC_FIELD_META_KEY] = {
|
||||
"ui_type": ui_type.value if isinstance(ui_type, PropertyType) else ui_type,
|
||||
"display_name": display_name,
|
||||
"llm_hint": llm_hint,
|
||||
"required": required,
|
||||
"placeholder": placeholder,
|
||||
"display_options": display_options,
|
||||
"options": options,
|
||||
"editor": editor,
|
||||
"extra": extra or {},
|
||||
"spec_exclude": spec_exclude,
|
||||
"min_value": min_value,
|
||||
"max_value": max_value,
|
||||
"min_length": min_length,
|
||||
"max_length": max_length,
|
||||
"pattern": pattern,
|
||||
}
|
||||
if spec_default is not _UNSET:
|
||||
json_schema_extra[_SPEC_FIELD_META_KEY]["spec_default"] = spec_default
|
||||
return Field(*field_args, json_schema_extra=json_schema_extra, **field_kwargs)
|
||||
|
||||
|
||||
def node_spec(
|
||||
*,
|
||||
name: str,
|
||||
display_name: str,
|
||||
description: str,
|
||||
category: NodeCategory,
|
||||
icon: str,
|
||||
llm_hint: str | None = None,
|
||||
version: str = "1.0.0",
|
||||
examples: list[NodeExample] | tuple[NodeExample, ...] = (),
|
||||
graph_constraints: GraphConstraints | None = None,
|
||||
property_order: list[str] | tuple[str, ...] = (),
|
||||
field_overrides: dict[str, dict[str, Any]] | None = None,
|
||||
) -> Callable[[type[BaseModel]], type[BaseModel]]:
|
||||
metadata = NodeSpecMetadata(
|
||||
name=name,
|
||||
display_name=display_name,
|
||||
description=description,
|
||||
category=category,
|
||||
icon=icon,
|
||||
llm_hint=llm_hint,
|
||||
version=version,
|
||||
examples=tuple(examples),
|
||||
graph_constraints=graph_constraints,
|
||||
property_order=tuple(property_order),
|
||||
field_overrides=field_overrides or {},
|
||||
)
|
||||
|
||||
def decorator(model_cls: type[BaseModel]) -> type[BaseModel]:
|
||||
setattr(model_cls, "__node_spec_metadata__", metadata)
|
||||
return model_cls
|
||||
|
||||
return decorator
|
||||
|
||||
|
||||
def build_spec(model_cls: type[BaseModel]) -> NodeSpec:
|
||||
metadata: NodeSpecMetadata | None = getattr(
|
||||
model_cls, "__node_spec_metadata__", None
|
||||
)
|
||||
if metadata is None:
|
||||
raise ValueError(f"{model_cls.__name__} is missing __node_spec_metadata__")
|
||||
|
||||
properties: list[PropertySpec] = []
|
||||
for name, field in model_cls.model_fields.items():
|
||||
prop = _build_property_spec(model_cls, name, field)
|
||||
if prop is not None:
|
||||
properties.append(prop)
|
||||
properties = _sort_properties(metadata.name, properties, metadata.property_order)
|
||||
|
||||
return NodeSpec(
|
||||
name=metadata.name,
|
||||
display_name=metadata.display_name,
|
||||
description=metadata.description,
|
||||
llm_hint=metadata.llm_hint,
|
||||
category=metadata.category,
|
||||
icon=metadata.icon,
|
||||
version=metadata.version,
|
||||
properties=properties,
|
||||
examples=list(metadata.examples),
|
||||
graph_constraints=metadata.graph_constraints,
|
||||
)
|
||||
|
||||
|
||||
def _sort_properties(
|
||||
spec_name: str,
|
||||
properties: list[PropertySpec],
|
||||
property_order: tuple[str, ...],
|
||||
) -> list[PropertySpec]:
|
||||
if not property_order:
|
||||
return properties
|
||||
|
||||
property_names = {prop.name for prop in properties}
|
||||
missing = [name for name in property_order if name not in property_names]
|
||||
if missing:
|
||||
raise ValueError(
|
||||
f"{spec_name}: property_order references unknown properties: {missing}"
|
||||
)
|
||||
|
||||
order_map = {name: idx for idx, name in enumerate(property_order)}
|
||||
ordered = sorted(
|
||||
enumerate(properties),
|
||||
key=lambda item: (order_map.get(item[1].name, len(order_map)), item[0]),
|
||||
)
|
||||
return [prop for _, prop in ordered]
|
||||
|
||||
|
||||
def _build_property_spec(
|
||||
owner_cls: type[BaseModel],
|
||||
field_name: str,
|
||||
field: FieldInfo,
|
||||
) -> PropertySpec | None:
|
||||
meta = _merged_field_meta(owner_cls, field_name, field)
|
||||
if meta.get("spec_exclude"):
|
||||
return None
|
||||
|
||||
prop_type = _resolve_property_type(field.annotation, meta)
|
||||
nested_properties = _resolve_nested_properties(field.annotation, prop_type)
|
||||
options = _resolve_options(field.annotation, meta, prop_type)
|
||||
min_value, max_value, min_length, max_length, pattern = _resolve_constraints(
|
||||
field, meta
|
||||
)
|
||||
|
||||
description = meta.get("description") or field.description
|
||||
if not description:
|
||||
raise ValueError(f"{owner_cls.__name__}.{field_name} is missing a description")
|
||||
|
||||
return PropertySpec(
|
||||
name=field_name,
|
||||
type=prop_type,
|
||||
display_name=meta.get("display_name") or _humanize_identifier(field_name),
|
||||
description=description,
|
||||
llm_hint=meta.get("llm_hint"),
|
||||
default=_resolve_default(field, meta),
|
||||
required=_resolve_required(field, meta),
|
||||
placeholder=meta.get("placeholder"),
|
||||
display_options=meta.get("display_options"),
|
||||
options=options,
|
||||
properties=nested_properties,
|
||||
min_value=min_value,
|
||||
max_value=max_value,
|
||||
min_length=min_length,
|
||||
max_length=max_length,
|
||||
pattern=pattern,
|
||||
editor=meta.get("editor"),
|
||||
extra=meta.get("extra") or {},
|
||||
)
|
||||
|
||||
|
||||
def _merged_field_meta(
|
||||
owner_cls: type[BaseModel],
|
||||
field_name: str,
|
||||
field: FieldInfo,
|
||||
) -> dict[str, Any]:
|
||||
field_meta = {}
|
||||
if isinstance(field.json_schema_extra, dict):
|
||||
field_meta = dict(field.json_schema_extra.get(_SPEC_FIELD_META_KEY, {}) or {})
|
||||
metadata: NodeSpecMetadata | None = getattr(
|
||||
owner_cls, "__node_spec_metadata__", None
|
||||
)
|
||||
override = (
|
||||
dict(metadata.field_overrides.get(field_name, {}) or {})
|
||||
if metadata is not None
|
||||
else {}
|
||||
)
|
||||
merged = dict(field_meta)
|
||||
merged.update(override)
|
||||
return merged
|
||||
|
||||
|
||||
def _resolve_property_type(annotation: Any, meta: dict[str, Any]) -> PropertyType:
|
||||
ui_type = meta.get("ui_type")
|
||||
if ui_type:
|
||||
return PropertyType(ui_type)
|
||||
|
||||
inner = _strip_optional(annotation)
|
||||
origin = get_origin(inner)
|
||||
args = get_args(inner)
|
||||
|
||||
if origin is list:
|
||||
item_type = _strip_optional(args[0]) if args else Any
|
||||
if isinstance(item_type, type) and issubclass(item_type, BaseModel):
|
||||
return PropertyType.fixed_collection
|
||||
raise ValueError(
|
||||
"List-valued fields must declare an explicit ui_type unless they wrap a "
|
||||
f"BaseModel row type (field annotation: {annotation!r})."
|
||||
)
|
||||
|
||||
if _is_enum(inner) or _is_literal(inner):
|
||||
return PropertyType.options
|
||||
|
||||
if inner in (str,):
|
||||
return PropertyType.string
|
||||
if inner in (int, float):
|
||||
return PropertyType.number
|
||||
if inner is bool:
|
||||
return PropertyType.boolean
|
||||
if inner in (dict, Any) or origin is dict:
|
||||
return PropertyType.json
|
||||
|
||||
raise ValueError(f"Unable to derive PropertyType for annotation {annotation!r}")
|
||||
|
||||
|
||||
def _resolve_nested_properties(
|
||||
annotation: Any,
|
||||
prop_type: PropertyType,
|
||||
) -> list[PropertySpec] | None:
|
||||
if prop_type != PropertyType.fixed_collection:
|
||||
return None
|
||||
|
||||
inner = _strip_optional(annotation)
|
||||
args = get_args(inner)
|
||||
if not args:
|
||||
raise ValueError(
|
||||
f"fixed_collection field annotation is missing row type: {annotation!r}"
|
||||
)
|
||||
row_type = _strip_optional(args[0])
|
||||
if not isinstance(row_type, type) or not issubclass(row_type, BaseModel):
|
||||
raise ValueError(
|
||||
f"fixed_collection rows must be BaseModel subclasses: {annotation!r}"
|
||||
)
|
||||
|
||||
properties: list[PropertySpec] = []
|
||||
for field_name, field in row_type.model_fields.items():
|
||||
prop = _build_property_spec(row_type, field_name, field)
|
||||
if prop is not None:
|
||||
properties.append(prop)
|
||||
return properties
|
||||
|
||||
|
||||
def _resolve_options(
|
||||
annotation: Any,
|
||||
meta: dict[str, Any],
|
||||
prop_type: PropertyType,
|
||||
) -> list[PropertyOption] | None:
|
||||
if prop_type not in (PropertyType.options, PropertyType.multi_options):
|
||||
return meta.get("options")
|
||||
|
||||
if meta.get("options"):
|
||||
return meta["options"]
|
||||
|
||||
inner = _strip_optional(annotation)
|
||||
if prop_type == PropertyType.multi_options:
|
||||
inner = _strip_optional(get_args(inner)[0])
|
||||
|
||||
if _is_enum(inner):
|
||||
return [
|
||||
PropertyOption(
|
||||
value=member.value, label=_humanize_option_label(member.value)
|
||||
)
|
||||
for member in inner
|
||||
]
|
||||
if _is_literal(inner):
|
||||
return [
|
||||
PropertyOption(value=value, label=_humanize_option_label(value))
|
||||
for value in get_args(inner)
|
||||
if value is not None
|
||||
]
|
||||
return None
|
||||
|
||||
|
||||
def _resolve_constraints(
|
||||
field: FieldInfo,
|
||||
meta: dict[str, Any],
|
||||
) -> tuple[float | None, float | None, int | None, int | None, str | None]:
|
||||
min_value = meta.get("min_value")
|
||||
max_value = meta.get("max_value")
|
||||
min_length = meta.get("min_length")
|
||||
max_length = meta.get("max_length")
|
||||
pattern = meta.get("pattern")
|
||||
|
||||
for item in field.metadata:
|
||||
if min_value is None:
|
||||
if hasattr(item, "ge") and item.ge is not None:
|
||||
min_value = item.ge
|
||||
elif hasattr(item, "gt") and item.gt is not None:
|
||||
min_value = item.gt
|
||||
if max_value is None:
|
||||
if hasattr(item, "le") and item.le is not None:
|
||||
max_value = item.le
|
||||
elif hasattr(item, "lt") and item.lt is not None:
|
||||
max_value = item.lt
|
||||
if (
|
||||
min_length is None
|
||||
and hasattr(item, "min_length")
|
||||
and item.min_length is not None
|
||||
):
|
||||
min_length = item.min_length
|
||||
if (
|
||||
max_length is None
|
||||
and hasattr(item, "max_length")
|
||||
and item.max_length is not None
|
||||
):
|
||||
max_length = item.max_length
|
||||
if pattern is None and hasattr(item, "pattern") and item.pattern is not None:
|
||||
pattern = item.pattern
|
||||
|
||||
return min_value, max_value, min_length, max_length, pattern
|
||||
|
||||
|
||||
def _resolve_default(field: FieldInfo, meta: dict[str, Any]) -> Any:
|
||||
if "spec_default" in meta:
|
||||
return meta["spec_default"]
|
||||
if field.default is not PydanticUndefined:
|
||||
return field.default
|
||||
return None
|
||||
|
||||
|
||||
def _resolve_required(field: FieldInfo, meta: dict[str, Any]) -> bool:
|
||||
if meta.get("required") is not None:
|
||||
return bool(meta["required"])
|
||||
return bool(field.is_required())
|
||||
|
||||
|
||||
def _strip_optional(annotation: Any) -> Any:
|
||||
origin = get_origin(annotation)
|
||||
if origin is None:
|
||||
return annotation
|
||||
|
||||
args = [arg for arg in get_args(annotation) if arg is not NoneType]
|
||||
if len(args) == 1 and len(args) != len(get_args(annotation)):
|
||||
return args[0]
|
||||
return annotation
|
||||
|
||||
|
||||
def _is_enum(annotation: Any) -> bool:
|
||||
return isinstance(annotation, type) and issubclass(annotation, Enum)
|
||||
|
||||
|
||||
def _is_literal(annotation: Any) -> bool:
|
||||
return get_origin(annotation) is Literal
|
||||
|
||||
|
||||
def _humanize_identifier(name: str) -> str:
|
||||
return name.replace("_", " ").strip().title()
|
||||
|
||||
|
||||
def _humanize_option_label(value: Any) -> str:
|
||||
if isinstance(value, str):
|
||||
return value.replace("_", " ").replace("-", " ").strip().title()
|
||||
return str(value)
|
||||
|
|
@ -1,203 +0,0 @@
|
|||
"""Spec for the QA Analysis node — runs an LLM quality review on the call
|
||||
transcript after completion."""
|
||||
|
||||
from api.services.workflow.node_specs._base import (
|
||||
DisplayOptions,
|
||||
GraphConstraints,
|
||||
NodeCategory,
|
||||
NodeExample,
|
||||
NodeSpec,
|
||||
PropertyOption,
|
||||
PropertySpec,
|
||||
PropertyType,
|
||||
)
|
||||
|
||||
DEFAULT_QA_SYSTEM_PROMPT = """You are a QA analyst evaluating a specific segment of a voice AI conversation.
|
||||
|
||||
## Node Purpose
|
||||
{{node_summary}}
|
||||
|
||||
## Previous Conversation Context (For start of conversation, previous conversation summary can be empty.)
|
||||
{{previous_conversation_summary}}
|
||||
|
||||
## Tags to evaluate
|
||||
|
||||
Examine the conversation carefully and identify which of the following tags apply:
|
||||
|
||||
- UNCLEAR_CONVERSATION - The conversation is not coherent or clear, messages don't connect logically
|
||||
- ASSISTANT_IN_LOOP - The assistant asks the same question multiple times or gets stuck repeating itself
|
||||
- ASSISTANT_REPLY_IMPROPER - The assistant did not reply properly to the user's question/query or seems confused by what the user said
|
||||
- USER_FRUSTRATED - The user seems angry, frustrated, or is complaining about something in the call
|
||||
- USER_NOT_UNDERSTANDING - The user explicitly says they don't understand or repeatedly asks for clarification
|
||||
- HEARING_ISSUES - Either party can't hear the other ("hello?", "are you there?", "can you hear me?")
|
||||
- DEAD_AIR - Unusually long silences in the conversation (use the timestamps to judge)
|
||||
- USER_REQUESTING_FEATURE - The user asks for something the assistant can't fulfill
|
||||
- ASSISTANT_LACKS_EMPATHY - The assistant ignores the user's personal situation or emotional state and continues pitching or pushing the agenda.
|
||||
- USER_DETECTS_AI - The user suspects or identifies that they are talking to an AI/robot/bot rather than a real human.
|
||||
|
||||
## Call metrics (pre-computed)
|
||||
|
||||
Use these alongside the transcript for your analysis:
|
||||
{{metrics}}
|
||||
|
||||
## Output format
|
||||
|
||||
Return ONLY a valid JSON object (no markdown):
|
||||
{
|
||||
"tags": [
|
||||
{
|
||||
"tag": "TAG_NAME",
|
||||
"reason": "Short reason with evidence from the transcript"
|
||||
}
|
||||
],
|
||||
"overall_sentiment": "positive|neutral|negative",
|
||||
"call_quality_score": <1-10>,
|
||||
"summary": "1-2 sentence summary of this segment"
|
||||
}
|
||||
|
||||
If no tags apply, return an empty tags list. Always provide sentiment, score, and summary."""
|
||||
|
||||
|
||||
SPEC = NodeSpec(
|
||||
name="qa",
|
||||
display_name="QA Analysis",
|
||||
description="Run LLM quality analysis on the call transcript.",
|
||||
llm_hint=(
|
||||
"Runs an LLM quality review on the call transcript after completion. "
|
||||
"Per-node analysis splits the conversation by node and evaluates each "
|
||||
"segment against the configured system prompt. Sampling, minimum "
|
||||
"duration, and voicemail filters are supported."
|
||||
),
|
||||
category=NodeCategory.integration,
|
||||
icon="ClipboardCheck",
|
||||
properties=[
|
||||
PropertySpec(
|
||||
name="name",
|
||||
type=PropertyType.string,
|
||||
display_name="Name",
|
||||
description="Short identifier for this QA configuration.",
|
||||
required=True,
|
||||
min_length=1,
|
||||
default="QA Analysis",
|
||||
),
|
||||
PropertySpec(
|
||||
name="qa_enabled",
|
||||
type=PropertyType.boolean,
|
||||
display_name="Enabled",
|
||||
description="When false, the QA run is skipped.",
|
||||
default=True,
|
||||
),
|
||||
PropertySpec(
|
||||
name="qa_system_prompt",
|
||||
type=PropertyType.string,
|
||||
display_name="System Prompt",
|
||||
description=(
|
||||
"Instructions to the QA reviewer LLM. Supports placeholders: "
|
||||
"`{node_summary}`, `{previous_conversation_summary}`, "
|
||||
"`{transcript}`, `{metrics}`."
|
||||
),
|
||||
editor="textarea",
|
||||
default=DEFAULT_QA_SYSTEM_PROMPT,
|
||||
),
|
||||
PropertySpec(
|
||||
name="qa_min_call_duration",
|
||||
type=PropertyType.number,
|
||||
display_name="Minimum Call Duration (seconds)",
|
||||
description="Calls shorter than this are skipped.",
|
||||
default=15,
|
||||
min_value=0,
|
||||
),
|
||||
PropertySpec(
|
||||
name="qa_voicemail_calls",
|
||||
type=PropertyType.boolean,
|
||||
display_name="Include Voicemail Calls",
|
||||
description="When false, calls flagged as voicemail are skipped.",
|
||||
default=False,
|
||||
),
|
||||
PropertySpec(
|
||||
name="qa_sample_rate",
|
||||
type=PropertyType.number,
|
||||
display_name="Sample Rate (%)",
|
||||
description=(
|
||||
"Percent of eligible calls QA'd. 100 means every call; lower "
|
||||
"values use random sampling."
|
||||
),
|
||||
default=100,
|
||||
min_value=1,
|
||||
max_value=100,
|
||||
),
|
||||
# ---- LLM configuration ----
|
||||
PropertySpec(
|
||||
name="qa_use_workflow_llm",
|
||||
type=PropertyType.boolean,
|
||||
display_name="Use Workflow's LLM",
|
||||
description=(
|
||||
"When true, the QA pass uses the same LLM the workflow runs "
|
||||
"with. Set false to specify a separate provider/model."
|
||||
),
|
||||
default=True,
|
||||
),
|
||||
PropertySpec(
|
||||
name="qa_provider",
|
||||
type=PropertyType.options,
|
||||
display_name="QA LLM Provider",
|
||||
description="LLM provider used for the QA pass.",
|
||||
display_options=DisplayOptions(show={"qa_use_workflow_llm": [False]}),
|
||||
options=[
|
||||
PropertyOption(value="openai", label="OpenAI"),
|
||||
PropertyOption(value="azure", label="Azure OpenAI"),
|
||||
PropertyOption(value="openrouter", label="OpenRouter"),
|
||||
PropertyOption(value="anthropic", label="Anthropic"),
|
||||
],
|
||||
),
|
||||
PropertySpec(
|
||||
name="qa_model",
|
||||
type=PropertyType.string,
|
||||
display_name="QA Model",
|
||||
description=(
|
||||
"Model identifier (e.g., 'gpt-4o', 'claude-sonnet-4-6'). "
|
||||
"Provider-specific."
|
||||
),
|
||||
display_options=DisplayOptions(show={"qa_use_workflow_llm": [False]}),
|
||||
default="default",
|
||||
),
|
||||
PropertySpec(
|
||||
name="qa_api_key",
|
||||
type=PropertyType.string,
|
||||
display_name="API Key",
|
||||
description="API key for the chosen provider.",
|
||||
display_options=DisplayOptions(show={"qa_use_workflow_llm": [False]}),
|
||||
),
|
||||
PropertySpec(
|
||||
name="qa_endpoint",
|
||||
type=PropertyType.url,
|
||||
display_name="Azure Endpoint",
|
||||
description="Required for the Azure provider.",
|
||||
display_options=DisplayOptions(
|
||||
show={"qa_use_workflow_llm": [False], "qa_provider": ["azure"]}
|
||||
),
|
||||
),
|
||||
],
|
||||
examples=[
|
||||
NodeExample(
|
||||
name="basic_qa",
|
||||
data={
|
||||
"name": "Compliance Check",
|
||||
"qa_enabled": True,
|
||||
"qa_system_prompt": (
|
||||
"You are a compliance reviewer. Review the transcript and "
|
||||
"produce a JSON object with `tags`, `summary`, "
|
||||
"`call_quality_score`, and `overall_sentiment`."
|
||||
),
|
||||
"qa_min_call_duration": 30,
|
||||
"qa_sample_rate": 100,
|
||||
},
|
||||
),
|
||||
],
|
||||
# QA runs post-call against the saved transcript (run_integrations
|
||||
# scans by type), never as a graph step. Reject any edge into or out
|
||||
# of a QA node.
|
||||
graph_constraints=GraphConstraints(
|
||||
min_incoming=0, max_incoming=0, min_outgoing=0, max_outgoing=0
|
||||
),
|
||||
)
|
||||
|
|
@ -1,250 +0,0 @@
|
|||
"""Spec for the Start Call node — the single entry point of every workflow.
|
||||
Carries greeting, pre-call data fetch, and the same prompt/extraction/tools
|
||||
fields as agent nodes."""
|
||||
|
||||
from api.services.workflow.node_specs._base import (
|
||||
DisplayOptions,
|
||||
GraphConstraints,
|
||||
NodeCategory,
|
||||
NodeExample,
|
||||
NodeSpec,
|
||||
PropertyOption,
|
||||
PropertySpec,
|
||||
PropertyType,
|
||||
)
|
||||
|
||||
SPEC = NodeSpec(
|
||||
name="startCall",
|
||||
display_name="Start Call",
|
||||
description="Entry point of the workflow — plays a greeting and opens the conversation.",
|
||||
llm_hint=(
|
||||
"The entry point of every workflow (exactly one required). Plays an "
|
||||
"optional greeting, can fetch context from an external API before "
|
||||
"the call begins, and executes the first conversational turn."
|
||||
),
|
||||
category=NodeCategory.call_node,
|
||||
icon="Play",
|
||||
properties=[
|
||||
PropertySpec(
|
||||
name="name",
|
||||
type=PropertyType.string,
|
||||
display_name="Name",
|
||||
description="Short identifier shown in the canvas and call logs.",
|
||||
required=True,
|
||||
min_length=1,
|
||||
default="Start Call",
|
||||
),
|
||||
# ---- Greeting (variant via greeting_type) ----
|
||||
PropertySpec(
|
||||
name="greeting_type",
|
||||
type=PropertyType.options,
|
||||
display_name="Greeting Type",
|
||||
description=(
|
||||
"Whether the optional greeting is spoken via TTS from text "
|
||||
"or played from a pre-recorded audio file."
|
||||
),
|
||||
default="text",
|
||||
options=[
|
||||
PropertyOption(value="text", label="Text (TTS)"),
|
||||
PropertyOption(value="audio", label="Pre-recorded Audio"),
|
||||
],
|
||||
),
|
||||
PropertySpec(
|
||||
name="greeting",
|
||||
type=PropertyType.string,
|
||||
display_name="Greeting Text",
|
||||
description=(
|
||||
"Text spoken via TTS at the start of the call. Supports "
|
||||
"{{template_variables}}. Leave empty to skip the greeting."
|
||||
),
|
||||
display_options=DisplayOptions(show={"greeting_type": ["text"]}),
|
||||
editor="textarea",
|
||||
placeholder="Hi {{first_name}}, this is Sarah from Acme.",
|
||||
),
|
||||
PropertySpec(
|
||||
name="greeting_recording_id",
|
||||
type=PropertyType.recording_ref,
|
||||
display_name="Greeting Recording",
|
||||
description="Pre-recorded audio file played at the start of the call.",
|
||||
llm_hint=(
|
||||
"Value is the `recording_id` string. Use the `list_recordings` "
|
||||
"MCP tool to discover available recordings."
|
||||
),
|
||||
display_options=DisplayOptions(show={"greeting_type": ["audio"]}),
|
||||
),
|
||||
PropertySpec(
|
||||
name="prompt",
|
||||
type=PropertyType.mention_textarea,
|
||||
display_name="Prompt",
|
||||
description=(
|
||||
"Agent system prompt for the opening turn. Supports "
|
||||
"{{template_variables}} from pre-call fetch and the initial context."
|
||||
),
|
||||
required=True,
|
||||
min_length=1,
|
||||
placeholder="Greet the caller warmly and ask how you can help today.",
|
||||
),
|
||||
# ---- Behavior toggles ----
|
||||
PropertySpec(
|
||||
name="allow_interrupt",
|
||||
type=PropertyType.boolean,
|
||||
display_name="Allow Interruption",
|
||||
description=("When true, the user can interrupt the agent mid-utterance."),
|
||||
default=False,
|
||||
),
|
||||
PropertySpec(
|
||||
name="add_global_prompt",
|
||||
type=PropertyType.boolean,
|
||||
display_name="Add Global Prompt",
|
||||
description=(
|
||||
"When true and a Global node exists, prepends the global "
|
||||
"prompt to this node's prompt at runtime."
|
||||
),
|
||||
default=True,
|
||||
),
|
||||
PropertySpec(
|
||||
name="delayed_start",
|
||||
type=PropertyType.boolean,
|
||||
display_name="Delayed Start",
|
||||
description=(
|
||||
"When true, the agent waits before speaking after pickup. "
|
||||
"Useful for outbound calls where the called party needs a "
|
||||
"moment to settle."
|
||||
),
|
||||
default=False,
|
||||
),
|
||||
PropertySpec(
|
||||
name="delayed_start_duration",
|
||||
type=PropertyType.number,
|
||||
display_name="Delay Duration (seconds)",
|
||||
description="Seconds to wait before the agent speaks. 0.1–10.",
|
||||
default=2.0,
|
||||
min_value=0.1,
|
||||
max_value=10.0,
|
||||
display_options=DisplayOptions(show={"delayed_start": [True]}),
|
||||
),
|
||||
# ---- Variable extraction ----
|
||||
PropertySpec(
|
||||
name="extraction_enabled",
|
||||
type=PropertyType.boolean,
|
||||
display_name="Enable Variable Extraction",
|
||||
description=(
|
||||
"When true, runs an LLM extraction pass on transition out of "
|
||||
"this node to capture variables from the opening turn."
|
||||
),
|
||||
default=False,
|
||||
),
|
||||
PropertySpec(
|
||||
name="extraction_prompt",
|
||||
type=PropertyType.string,
|
||||
display_name="Extraction Prompt",
|
||||
description="Overall instructions guiding variable extraction.",
|
||||
display_options=DisplayOptions(show={"extraction_enabled": [True]}),
|
||||
editor="textarea",
|
||||
),
|
||||
PropertySpec(
|
||||
name="extraction_variables",
|
||||
type=PropertyType.fixed_collection,
|
||||
display_name="Variables to Extract",
|
||||
description=(
|
||||
"Each entry declares one variable to capture, with its name, "
|
||||
"data type, and per-variable extraction hint."
|
||||
),
|
||||
display_options=DisplayOptions(show={"extraction_enabled": [True]}),
|
||||
properties=[
|
||||
PropertySpec(
|
||||
name="name",
|
||||
type=PropertyType.string,
|
||||
display_name="Variable Name",
|
||||
description="snake_case identifier used downstream.",
|
||||
required=True,
|
||||
),
|
||||
PropertySpec(
|
||||
name="type",
|
||||
type=PropertyType.options,
|
||||
display_name="Type",
|
||||
description="Data type of the extracted value.",
|
||||
required=True,
|
||||
default="string",
|
||||
options=[
|
||||
PropertyOption(value="string", label="String"),
|
||||
PropertyOption(value="number", label="Number"),
|
||||
PropertyOption(value="boolean", label="Boolean"),
|
||||
],
|
||||
),
|
||||
PropertySpec(
|
||||
name="prompt",
|
||||
type=PropertyType.string,
|
||||
display_name="Extraction Hint",
|
||||
description="Per-variable hint describing what to look for.",
|
||||
editor="textarea",
|
||||
),
|
||||
],
|
||||
),
|
||||
# ---- Tools / documents ----
|
||||
PropertySpec(
|
||||
name="tool_uuids",
|
||||
type=PropertyType.tool_refs,
|
||||
display_name="Tools",
|
||||
description="Tools the agent can invoke during the opening turn.",
|
||||
llm_hint="List of tool UUIDs from `list_tools`.",
|
||||
),
|
||||
PropertySpec(
|
||||
name="document_uuids",
|
||||
type=PropertyType.document_refs,
|
||||
display_name="Knowledge Base Documents",
|
||||
description="Documents the agent can reference.",
|
||||
llm_hint="List of document UUIDs from `list_documents`.",
|
||||
),
|
||||
# ---- Pre-call data fetch (advanced) ----
|
||||
PropertySpec(
|
||||
name="pre_call_fetch_enabled",
|
||||
type=PropertyType.boolean,
|
||||
display_name="Pre-Call Data Fetch",
|
||||
description=(
|
||||
"When true, makes a POST request to an external API before "
|
||||
"the call starts and merges the JSON response into the call "
|
||||
"context as template variables."
|
||||
),
|
||||
default=False,
|
||||
),
|
||||
PropertySpec(
|
||||
name="pre_call_fetch_url",
|
||||
type=PropertyType.url,
|
||||
display_name="Endpoint URL",
|
||||
description=(
|
||||
"URL the pre-call POST request is sent to. The request body "
|
||||
"includes caller and called numbers."
|
||||
),
|
||||
display_options=DisplayOptions(show={"pre_call_fetch_enabled": [True]}),
|
||||
placeholder="https://api.example.com/customer-lookup",
|
||||
),
|
||||
PropertySpec(
|
||||
name="pre_call_fetch_credential_uuid",
|
||||
type=PropertyType.credential_ref,
|
||||
display_name="Authentication",
|
||||
description="Optional credential attached to the pre-call request.",
|
||||
llm_hint="Credential UUID from `list_credentials`.",
|
||||
display_options=DisplayOptions(show={"pre_call_fetch_enabled": [True]}),
|
||||
),
|
||||
],
|
||||
examples=[
|
||||
NodeExample(
|
||||
name="warm_greeting",
|
||||
data={
|
||||
"name": "Greeting",
|
||||
"prompt": "Greet warmly and ask the caller's reason for calling.",
|
||||
"greeting_type": "text",
|
||||
"greeting": "Hi {{first_name}}, this is Sarah from Acme.",
|
||||
"allow_interrupt": True,
|
||||
},
|
||||
),
|
||||
],
|
||||
# `min_outgoing` is intentionally unset: a startCall is allowed to
|
||||
# sit on the canvas without an outgoing edge (e.g. a workflow with
|
||||
# just a greeting). Only constraint: nothing flows INTO the start.
|
||||
graph_constraints=GraphConstraints(
|
||||
min_incoming=0,
|
||||
max_incoming=0,
|
||||
),
|
||||
)
|
||||
|
|
@ -1,79 +0,0 @@
|
|||
"""Spec for the API Trigger node — exposes a public webhook URL that
|
||||
external systems can hit to launch the workflow."""
|
||||
|
||||
from api.services.workflow.node_specs._base import (
|
||||
GraphConstraints,
|
||||
NodeCategory,
|
||||
NodeExample,
|
||||
NodeSpec,
|
||||
PropertySpec,
|
||||
PropertyType,
|
||||
)
|
||||
|
||||
SPEC = NodeSpec(
|
||||
name="trigger",
|
||||
display_name="API Trigger",
|
||||
description=("Public HTTP endpoints that launch the workflow."),
|
||||
llm_hint=(
|
||||
"Exposes two public HTTP POST endpoints derived from the auto-generated "
|
||||
"`trigger_path`:\n"
|
||||
" • Production: `<backend>/api/v1/public/agent/<trigger_path>` — runs "
|
||||
"the published agent. Use this from production systems.\n"
|
||||
" • Test: `<backend>/api/v1/public/agent/test/<trigger_path>` — runs "
|
||||
"the latest draft, useful for verifying changes before publishing. "
|
||||
"Falls back to the published agent when no draft exists.\n"
|
||||
"Both require an API key in the `X-API-Key` header.\n"
|
||||
"Request body fields:\n"
|
||||
" • `phone_number` (string, required) — destination to dial.\n"
|
||||
" • `initial_context` (object, optional) — merged into the run's "
|
||||
"initial context.\n"
|
||||
" • `telephony_configuration_id` (int, optional) — pick a specific "
|
||||
"telephony configuration for the call. Must belong to the same "
|
||||
"organization as the trigger. When omitted, the org's default "
|
||||
"outbound configuration is used."
|
||||
),
|
||||
category=NodeCategory.trigger,
|
||||
icon="Webhook",
|
||||
properties=[
|
||||
PropertySpec(
|
||||
name="name",
|
||||
type=PropertyType.string,
|
||||
display_name="Name",
|
||||
description="Short identifier shown in the canvas. No runtime effect.",
|
||||
required=True,
|
||||
min_length=1,
|
||||
default="API Trigger",
|
||||
),
|
||||
PropertySpec(
|
||||
name="enabled",
|
||||
type=PropertyType.boolean,
|
||||
display_name="Enabled",
|
||||
description="When false, the trigger URL returns 404.",
|
||||
default=True,
|
||||
),
|
||||
PropertySpec(
|
||||
name="trigger_path",
|
||||
type=PropertyType.string,
|
||||
display_name="Trigger Path",
|
||||
description=(
|
||||
"Auto-generated UUID-style path segment that uniquely "
|
||||
"identifies this trigger. Used in both URLs:\n"
|
||||
" • Production: `/api/v1/public/agent/<trigger_path>` — "
|
||||
"executes the published agent.\n"
|
||||
" • Test: `/api/v1/public/agent/test/<trigger_path>` — "
|
||||
"executes the latest draft.\n"
|
||||
"Do not edit manually."
|
||||
),
|
||||
),
|
||||
],
|
||||
examples=[
|
||||
NodeExample(
|
||||
name="default",
|
||||
data={"name": "Inbound Trigger", "enabled": True},
|
||||
),
|
||||
],
|
||||
graph_constraints=GraphConstraints(
|
||||
min_incoming=0,
|
||||
max_incoming=0,
|
||||
),
|
||||
)
|
||||
|
|
@ -1,133 +0,0 @@
|
|||
"""Spec for the Webhook node — sends an HTTP request to an external system
|
||||
after the workflow completes."""
|
||||
|
||||
from api.services.workflow.node_specs._base import (
|
||||
GraphConstraints,
|
||||
NodeCategory,
|
||||
NodeExample,
|
||||
NodeSpec,
|
||||
PropertyOption,
|
||||
PropertySpec,
|
||||
PropertyType,
|
||||
)
|
||||
|
||||
SPEC = NodeSpec(
|
||||
name="webhook",
|
||||
display_name="Webhook",
|
||||
description="Send HTTP request after the workflow completes.",
|
||||
llm_hint=(
|
||||
"Sends an HTTP request to an external system after the workflow "
|
||||
"completes. The payload is a Jinja-templated JSON body with access "
|
||||
"to `workflow_run_id`, `initial_context`, `gathered_context`, "
|
||||
"`annotations`, and call metadata."
|
||||
),
|
||||
category=NodeCategory.integration,
|
||||
icon="Link2",
|
||||
properties=[
|
||||
PropertySpec(
|
||||
name="name",
|
||||
type=PropertyType.string,
|
||||
display_name="Name",
|
||||
description="Short identifier shown in the canvas and run logs.",
|
||||
required=True,
|
||||
min_length=1,
|
||||
default="Webhook",
|
||||
),
|
||||
PropertySpec(
|
||||
name="enabled",
|
||||
type=PropertyType.boolean,
|
||||
display_name="Enabled",
|
||||
description="When false, the webhook is skipped at run time.",
|
||||
default=True,
|
||||
),
|
||||
PropertySpec(
|
||||
name="http_method",
|
||||
type=PropertyType.options,
|
||||
display_name="HTTP Method",
|
||||
description="HTTP verb used for the outbound request.",
|
||||
default="POST",
|
||||
options=[
|
||||
PropertyOption(value="GET", label="GET"),
|
||||
PropertyOption(value="POST", label="POST"),
|
||||
PropertyOption(value="PUT", label="PUT"),
|
||||
PropertyOption(value="PATCH", label="PATCH"),
|
||||
PropertyOption(value="DELETE", label="DELETE"),
|
||||
],
|
||||
),
|
||||
PropertySpec(
|
||||
name="endpoint_url",
|
||||
type=PropertyType.url,
|
||||
display_name="Endpoint URL",
|
||||
description="URL the request is sent to.",
|
||||
placeholder="https://api.example.com/webhook",
|
||||
),
|
||||
PropertySpec(
|
||||
name="credential_uuid",
|
||||
type=PropertyType.credential_ref,
|
||||
display_name="Authentication",
|
||||
description="Optional credential applied as the Authorization header.",
|
||||
llm_hint="Credential UUID from `list_credentials`.",
|
||||
),
|
||||
PropertySpec(
|
||||
name="custom_headers",
|
||||
type=PropertyType.fixed_collection,
|
||||
display_name="Custom Headers",
|
||||
description="Additional HTTP headers to include with the request.",
|
||||
properties=[
|
||||
PropertySpec(
|
||||
name="key",
|
||||
type=PropertyType.string,
|
||||
display_name="Header Name",
|
||||
description="HTTP header name (e.g., 'X-Source').",
|
||||
required=True,
|
||||
),
|
||||
PropertySpec(
|
||||
name="value",
|
||||
type=PropertyType.string,
|
||||
display_name="Header Value",
|
||||
description="Header value (supports {{template_variables}}).",
|
||||
required=True,
|
||||
),
|
||||
],
|
||||
),
|
||||
PropertySpec(
|
||||
name="payload_template",
|
||||
type=PropertyType.json,
|
||||
display_name="Payload Template",
|
||||
description=(
|
||||
"JSON body of the request. Values are Jinja-rendered against "
|
||||
"the run context — `{{workflow_run_id}}`, "
|
||||
"`{{gathered_context.foo}}`, `{{annotations.qa_xxx}}`, etc."
|
||||
),
|
||||
default={
|
||||
"call_id": "{{workflow_run_id}}",
|
||||
"first_name": "{{initial_context.first_name}}",
|
||||
"rsvp": "{{gathered_context.rsvp}}",
|
||||
"duration": "{{cost_info.call_duration_seconds}}",
|
||||
"recording_url": "{{recording_url}}",
|
||||
"transcript_url": "{{transcript_url}}",
|
||||
},
|
||||
),
|
||||
],
|
||||
examples=[
|
||||
NodeExample(
|
||||
name="post_to_crm",
|
||||
data={
|
||||
"name": "Notify CRM",
|
||||
"enabled": True,
|
||||
"http_method": "POST",
|
||||
"endpoint_url": "https://crm.example.com/calls",
|
||||
"payload_template": {
|
||||
"run_id": "{{workflow_run_id}}",
|
||||
"outcome": "{{gathered_context.call_disposition}}",
|
||||
},
|
||||
},
|
||||
),
|
||||
],
|
||||
# Webhooks fire post-call (run_integrations scans nodes by type),
|
||||
# never as a graph step. Reject any edge into or out of a webhook so
|
||||
# the editor can't wire one into the conversation flow.
|
||||
graph_constraints=GraphConstraints(
|
||||
min_incoming=0, max_incoming=0, min_outgoing=0, max_outgoing=0
|
||||
),
|
||||
)
|
||||
|
|
@ -540,7 +540,7 @@ class PipecatEngine:
|
|||
node = self.workflow.nodes[node_id]
|
||||
|
||||
logger.debug(
|
||||
f"Executing node: name: {node.name} is_static: {node.is_static} allow_interrupt: {node.allow_interrupt} is_end: {node.is_end}"
|
||||
f"Executing node: name: {node.name} allow_interrupt: {node.allow_interrupt} is_end: {node.is_end}"
|
||||
)
|
||||
|
||||
# Track previous node for transition event
|
||||
|
|
@ -595,11 +595,8 @@ class PipecatEngine:
|
|||
)
|
||||
await asyncio.sleep(delay_duration)
|
||||
|
||||
if node.is_static:
|
||||
raise ValueError("Static nodes are not supported!")
|
||||
else:
|
||||
# Setup LLM Context with Prompts and Functions
|
||||
await self._setup_llm_context(node)
|
||||
# Setup LLM context with prompts and functions.
|
||||
await self._setup_llm_context(node)
|
||||
|
||||
def get_start_greeting(self) -> Optional[tuple[str, Optional[str]]]:
|
||||
"""Return the greeting info for the start node, or None if not configured.
|
||||
|
|
@ -626,19 +623,13 @@ class PipecatEngine:
|
|||
|
||||
async def _handle_end_node(self, node: Node) -> None:
|
||||
"""Handle end node execution."""
|
||||
if node.is_static:
|
||||
raise ValueError("Static nodes are not supported!")
|
||||
else:
|
||||
# Setup LLM Context with Prompts and Functions
|
||||
await self._setup_llm_context(node)
|
||||
# Setup LLM context with prompts and functions.
|
||||
await self._setup_llm_context(node)
|
||||
|
||||
async def _handle_agent_node(self, node: Node) -> None:
|
||||
"""Handle agent node execution."""
|
||||
if node.is_static:
|
||||
raise ValueError("Static nodes are not supported!")
|
||||
else:
|
||||
# Setup LLM Context with Prompts and Functions
|
||||
await self._setup_llm_context(node)
|
||||
# Setup LLM context with prompts and functions.
|
||||
await self._setup_llm_context(node)
|
||||
|
||||
async def end_call_with_reason(
|
||||
self,
|
||||
|
|
|
|||
|
|
@ -1,10 +1,11 @@
|
|||
import re
|
||||
from collections import Counter
|
||||
from typing import Dict, List, Set
|
||||
from typing import Any, Dict, List, Set
|
||||
|
||||
from api.services.workflow.dto import EdgeDataDTO, NodeType, ReactFlowDTO
|
||||
from api.services.workflow.errors import ItemKind, WorkflowError
|
||||
from api.services.workflow.node_specs import REGISTRY
|
||||
from api.services.workflow.node_data import BaseNodeData
|
||||
from api.services.workflow.node_specs import get_spec
|
||||
|
||||
# Regex for matching {{ variable }} template placeholders.
|
||||
# Captures: group(1) = variable path, group(2) = filter name, group(3) = filter value.
|
||||
|
|
@ -62,7 +63,7 @@ class Edge:
|
|||
|
||||
|
||||
class Node:
|
||||
def __init__(self, id: str, node_type: NodeType, data):
|
||||
def __init__(self, id: str, node_type: str, data: BaseNodeData):
|
||||
self.id, self.node_type, self.data = id, node_type, data
|
||||
self.out: Dict[str, "Node"] = {} # forward nodes
|
||||
self.out_edges: List[Edge] = [] # forward edges with properties
|
||||
|
|
@ -75,7 +76,6 @@ class Node:
|
|||
# Type-specific fields — read with getattr so this works for every
|
||||
# node variant in the discriminated union.
|
||||
self.prompt = getattr(data, "prompt", None)
|
||||
self.is_static = getattr(data, "is_static", False)
|
||||
self.allow_interrupt = getattr(data, "allow_interrupt", False)
|
||||
self.extraction_enabled = getattr(data, "extraction_enabled", False)
|
||||
self.extraction_prompt = getattr(data, "extraction_prompt", None)
|
||||
|
|
@ -84,7 +84,6 @@ class Node:
|
|||
self.greeting = getattr(data, "greeting", None)
|
||||
self.greeting_type = getattr(data, "greeting_type", None)
|
||||
self.greeting_recording_id = getattr(data, "greeting_recording_id", None)
|
||||
self.detect_voicemail = getattr(data, "detect_voicemail", False)
|
||||
self.delayed_start = getattr(data, "delayed_start", False)
|
||||
self.delayed_start_duration = getattr(data, "delayed_start_duration", None)
|
||||
self.tool_uuids = getattr(data, "tool_uuids", None)
|
||||
|
|
@ -106,11 +105,11 @@ class WorkflowGraph:
|
|||
"""
|
||||
|
||||
def __init__(self, dto: ReactFlowDTO):
|
||||
# build adjacency list. n.type comes off the discriminated-union
|
||||
# variant as a literal string; coerce to NodeType for downstream
|
||||
# comparisons.
|
||||
# Build adjacency list from validated DTO nodes. Core node comparisons
|
||||
# still use NodeType string enums; integration nodes remain plain
|
||||
# strings and resolve constraints through node specs.
|
||||
self.nodes: Dict[str, Node] = {
|
||||
n.id: Node(n.id, NodeType(n.type), n.data) for n in dto.nodes
|
||||
n.id: Node(n.id, n.type, n.data) for n in dto.nodes
|
||||
}
|
||||
|
||||
# Store all edges
|
||||
|
|
@ -140,7 +139,7 @@ class WorkflowGraph:
|
|||
# Get a reference to the global node
|
||||
try:
|
||||
self.global_node_id = [
|
||||
n.id for n in dto.nodes if n.type == NodeType.globalNode
|
||||
n.id for n in dto.nodes if n.type == NodeType.globalNode.value
|
||||
][0]
|
||||
except IndexError:
|
||||
self.global_node_id = None
|
||||
|
|
@ -250,7 +249,7 @@ class WorkflowGraph:
|
|||
def _assert_global_node(self):
|
||||
errors: list[WorkflowError] = []
|
||||
global_node = [
|
||||
n for n in self.nodes.values() if n.node_type == NodeType.globalNode
|
||||
n for n in self.nodes.values() if n.node_type == NodeType.globalNode.value
|
||||
]
|
||||
if not len(global_node) <= 1:
|
||||
errors.append(
|
||||
|
|
@ -282,7 +281,7 @@ class WorkflowGraph:
|
|||
in_deg[m.id] += 1
|
||||
|
||||
for n in self.nodes.values():
|
||||
spec = REGISTRY.get(n.node_type.value)
|
||||
spec = get_spec(n.node_type)
|
||||
if spec is None or spec.graph_constraints is None:
|
||||
continue
|
||||
gc = spec.graph_constraints
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue